Re: [Fwd: occasions during pregnancy]
Hi,
Within subject variability has two parts -- a random component that is you have tried to describe using between occasion variability (AKA IOV) -- and a fixed (or predictable) component that in your case is associated with each stage of pregnancy.
Your code only has the random component for within subject variability in it. I suggest you include a stage of pregnancy covariate effect on your PK parameters. That would then resolve the discrepancy in goodness of fit you found between treating each stage as a different subject compared with only using random BOV to explain differences.
Whether the random differences in parameter variability are larger within subjects ("IOV") compared to within subjects ("IIV")has no intrinsic importance and you do not have to make any assumption about their relative magnitude. It is just a description of the way things are. For example within subject variability in oral bioavailability will often be bigger than between subject variability because it is determined by day to day vagaries of absorption. On the other hand between subject variability in volume of distribution will usually be larger than within subject variability because there are usually rather small random changes from day to day.
Note that in your case the fixed effect (predictable) changes in volume in different stages of pregnancy may be quite large and account for a large fraction of your current estimates using a random effect ("IOV") alone. The fixed effect changes in pregnancy are likely to be the most interesting part of your analysis to your clinical colleagues so don't forget them :-)
Best wishes,
Nick
Quoted reply history
On 2/03/2011 5:59 a.m., Kevin Dykstra wrote:
> Paul,
> You might try plotting your etas 6-9 vs. trimester (coded at four levels) to
> ensure that the IOV is truly random by occasion, as your model assumes.
> Obviously, it is not unheard of that the IOV should be much larger than IIV,
> but I wouldn't start with that assumption. Usually there is at least some
> correlation within an individual. Good luck.
> Kevin
>
> Kevin Dykstra, PhD, FCP
>
> +1 978.655.1943 (O)
> +1 978.289.2987 (M)
> [email protected] | http://qPharmetra.com
>
> -----Original Message-----
> From: [email protected] [mailto:[email protected]] On
> Behalf Of [email protected]
> Sent: Tuesday, March 01, 2011 10:53 AM
> To: nm nm
> Subject: [NMusers] [Fwd: occasions during pregnancy]
>
> ---------------------------- Original Message ----------------------------
> Subject: occasions during pregnancy
> From: [email protected]
> Date: Tue, March 1, 2011 10:49 am
> To: "nm nm"<[email protected]>
> --------------------------------------------------------------------------
>
> Hi all nmusers,
>
> I thank all who responded my questions yesterday. Almost all the responses
> suggested that several occasions of one patient should be put under one ID
> #. I re-code my control stream and adjusted the data file as following:
>
> $PK
> K12 = THETA(1)*EXP(ETA(1))
> CL=
> THETA(2)*EXP(ETA(2)+ETA(6)*TRI1+ETA(7)*TRI2+ETA(8)*TRI3+ETA(9)*TRI4)
> $OMEGA
> .8;
> .1 .8;
> .1 .1 .8;
> .1 .1 .1 .8;
> .1 .1 .1 .1 .8;
> $OMEGA BLOCK(1) 0.9;
> $OMEGA BLOCK(1) SAME;
> $OMEGA BLOCK(1) SAME;
> $OMEGA BLOCK(1) SAME;
>
> where TRI1,TRI2,TRI3, and TRI4 are different stages of pregnancy.
>
> This model fits poorly for the data (from the plot of PRED, IPRED VS. DV),
> although the estimates are stable and reasonable.
>
> If I treat the different occasions as different patients, ignoring the
> correlation within the same patients, then the model fits quite well and the
> results are reasonable.
>
> I also noticed one note from Lewis Sheiner:
>
> Note that, as happens more often, at least with human data, than one might
> have thought, the IOV>IIV, then treating each occaasion as though it were a
> distinct individual is a reasonable approximation.
>
> --------------Date: Wed, 17 Nov 1999 13:57:18 -0800
> From: Lewis Sheiner<[email protected]>
> Subject: Re: repeating cases---------
>
> The parameters during pregnancy change quite large, so I am not sure if it
> is a reasonalble approximation to treat occasions as distinct individual, or
> I have to search the better models of putting those occasions under one ID?
> and what is the direction to improve the model?
>
> Any suggestion is greatly appreciated.
>
> Paul
>
> School of Pharmacy
> University of Pittsburgh
> 716 Salk Hall
> 3501 Terrace Street
> Pittsburgh, PA 15261
> Phone: 412-648-8546
> E-mail: [email protected]
>
> Yuanyue (Paul) Gao
>
> School of Pharmacy
> University of Pittsburgh
> 716 Salk Hall
> 3501 Terrace Street
> Pittsburgh, PA 15261
> Phone: 412-648-8546
> E-mail: [email protected]
--
Nick Holford, Professor Clinical Pharmacology
Dept Pharmacology& Clinical Pharmacology
University of Auckland,85 Park Rd,Private Bag 92019,Auckland,New Zealand
tel:+64(9)923-6730 fax:+64(9)373-7090 mobile:+64(21)46 23 53
email: [email protected]
http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford